Pros and cons

Base R:

Pros

  • it is there
  • works with everything
  • works in every version of R

Cons

  • unreadable
  • {{}}())([]])

Tidyverse:

Pros

  • readibility
  • speed (almost always)
  • simplicity
  • usability
    • consistency (eg. The first formal argument is always a data frame that provides the function’s input)
    • coverage (full workflow)
    • help (large user community)
    • potential to increase usability

Cons

  • needs installing
  • integration with other packages (eg. Bioconductor)
  • dependent development (close coordination within tidyverse)

https://www.tidyverse.org/ https://rviews.rstudio.com/2017/06/08/what-is-the-tidyverse/

Readability with %>%

With base R the functions are nested which means that we need to read the operations from inside out rather than in the order they are performed which using %>% “pipes” demonstrates nicely.

x <- c(0.109, 0.359, 0.63, 0.996, 0.515, 0.142, 0.017, 0.829, 0.907)

# base
round(exp(diff(log(x))), 1)
## [1]  3.3  1.8  1.6  0.5  0.3  0.1 48.8  1.1
# magrittr
library(magrittr)
x %>% log %>%
    diff %>%
    exp %>%
    round(1)
## [1]  3.3  1.8  1.6  0.5  0.3  0.1 48.8  1.1

Looking at data

Usually tibble displays data nicer than base R (not such a problem with notebooks), the difference is when looking at long character string.

Example

Modified from: http://www.onthelambda.com/2014/02/10/how-dplyr-replaced-my-most-common-r-idioms/

Data

diamonds: A dataset containing the prices and other attributes of almost 54,000 diamonds. http://ggplot2.tidyverse.org/reference/diamonds.html

Initial plots

# base
plot(diamonds$carat, diamonds$price, col = diamonds$color,
    pch = as.numeric(diamonds$cut))

# ggplot2
ggplot(diamonds, aes(carat, price, col = color, shape = cut)) +
    geom_point()

Filtering rows

Again, both work well with dplyr working faster and in fewer characters. The Base R helps us with column names if we use tab completion, while with dplyr we need to know the exact column names.

# base R
diamonds_base <- diamonds[diamonds$z > 0 & diamonds$cut != "Fair",]

# dplyr
diamonds_tdyv <- filter(diamonds,
                        z>0,
                        cut != "Fair")

Arranging and ordering

# base R
diamonds_base <- diamonds_base[order(diamonds_base$price, 
                                     decreasing=TRUE), ]
# dplyr
diamonds_tdyv <- arrange(diamonds_tdyv, desc(price))

Selecting columns

# base R
names <- c(colnames(diamonds_base)[grepl("^c", colnames(diamonds_base))], "price", "x", "y", "z")
diamonds_base <- diamonds_base[,names]

# dplyr
diamonds_tdyv <- select(diamonds_tdyv, starts_with("c"), price, x, y, z)

Creating new columns

# base R
diamonds_base$mass <- diamonds_base$carat * 0.2
diamonds_base$size <- diamonds_base$x * diamonds_base$y * diamonds_base$z

# dplyr
diamonds_tdyv <- mutate(diamonds_tdyv, mass = carat * 0.2) 
diamonds_tdyv <- mutate(diamonds_tdyv, size = x * y * z)

Aggregation and summarization

# base R
summary1 <- aggregate(price ~ color,
                      data=diamonds_base,
                      FUN=mean)
summary2 <- aggregate(price ~ color,
                      data=diamonds_base,
                      FUN=length)
summary_diamonds_base <- merge(summary1, summary2, 
                                 by="color")

# dplyr
by.color <- group_by(diamonds_tdyv, color)
summary_diamonds_tdyv <- summarise(by.color,
                                   num_color = n(),
                                   price = mean(price))

All together

tidyverse really shows it’s selling point here as it is much shorter and much more readable.

# base R
names <- c(colnames(diamonds_base)[grepl("^c", colnames(diamonds_base))], "price", "x", "y", "z")
diamonds_base <- diamonds[diamonds$z>0 & 
                            diamonds$cut!="Fair", 
                          names]
diamonds_base <- diamonds_base[order(diamonds_base$carat, 
                                     decreasing=TRUE), ]
diamonds_base$mass <- diamonds_base$carat * 0.2
diamonds_base$size <- diamonds_base$x * diamonds_base$y * diamonds_base$z
summary1 <- aggregate(price ~ color,
                      data=diamonds_base,
                      FUN=mean)
summary2 <- aggregate(price ~ color,
                      data=diamonds_base,
                      FUN=length)
summary_diamonds_base <- merge(summary1, summary2, 
                                 by="color")


# tidyverse
diamonds %>% 
  filter(z>0, cut!="Fair") %>%
  arrange(desc(carat)) %>%
  select(starts_with("c"), price, x, y, z) %>%
  mutate(mass = carat * 0.2, size = x * y * z) %>%
  group_by(color) %>%
  summarise(num_color = n(), price = mean(price))

Quick plots

# base
plot(diamonds_base$carat, diamonds_base$price, col = diamonds_base$color)

plot(diamonds_base$carat, diamonds_base$price, col = diamonds_base$clarity)

plot(diamonds_base$mass, diamonds_base$size, col = diamonds_base$carat)

# ggplot2
ggplot(diamonds, aes(carat, price, col = clarity, shape = cut)) +
    geom_point()

ggplot(diamonds, aes(carat, price, col = color, shape = cut)) +
    geom_point()

diamonds %>% 
  filter(z>0, cut!="Fair") %>%
  arrange(desc(carat)) %>%
  select(starts_with("c"), price, x, y, z) %>%
  mutate(mass = carat * 0.2, size = x * y * z) %>%
  group_by(color) %>%
  ggplot(aes(mass, size, col=carat)) + geom_point()

Extra resources https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf http://www.significantdigits.org/2017/10/switching-from-base-r-to-tidyverse/ Table of base to tidyverse function conversion https://rviews.rstudio.com/2017/06/08/what-is-the-tidyverse/ [R for Data Science] (http://r4ds.had.co.nz/)

The source for this tutorial is on github.